User equipment detection for uplink random access in dispersive fading environments

ABSTRACT

Systems and methods for detecting potentially active user equipment (UE) in a network are provided. A set of potentially active UEs is detected using an iterative method. In a first iteration UE detection is performed using compressed sensing (CS) based on first pilot sequences to detect a first set of potentially active UEs. Channel estimation is then performed for the first set of potentially active UEs. In subsequent iterations, UE detection is performed using CS to detect another set, typically reduced in size, of potentially active UEs using results of the channel estimation from the previous iteration. Channel estimation is then again performed for the new set of potentially active UEs. After the last iteration, the set of potentially active UEs detected in the last iteration is output as the determined set of potentially active UEs along with channel estimates for the set of potentially active UEs determined in last iteration.

FIELD

The application relates to user equipment detection for uplink randomaccess.

BACKGROUND

User equipment (UE) detection is an important ingredient of any uplinkrandom access system. Compressed sensing (CS) is a tool for UE detectionin scenarios where UE activity is sparse in the sense that from a largenumber of UEs that could attempt to access the system at a giveninstant, typically a relatively small number are in fact attempting toaccess the system. In other words, a small number of UEs among a largepool of UEs are simultaneously active.

A standard CS problem involves solving an underdetermined set of (noisyor noiseless) equations in terms of an unknown sparse vector based on anumber of observations. The number of nonzero elements in the unknownvector is much less than the number of observations. The set ofequations can be expressed as follows:

y _(M×1) =P _(M×N)·h_(N×1) +n _(M×1)

where the elements of the equation are:

y_(M×1)=is a set of M observations;

h_(N×1) is a set of N unknowns;

P_(M×N) is a matrix that defines linear combinations of the unknowns;

n_(M×1) is a set of noise components.

with K<<M<N, where K is the number of nonzero elements of h.

The CS problem is usually cast as an optimization problem. One typicalexample is the following convex optimization problem

$\hat{h} = {{\underset{h}{{\arg \mspace{11mu} \min}\;}\frac{1}{2}{{y - {Ph}}}_{2}^{2}} + {\lambda {h}_{1}}}$

∥·∥₂ and ∥·∥₁ denote I₂-norm and I₁-norm of a vector, respectively,defined as

∥x∥ ₂=√{square root over (Σ_(i) |x _(i)|²)}and ∥x∥ ₁ =Σ _(i) |x _(i)∥

The UE detection problem can be recast as a CS problem based on thefollowing set of equations:

y _(M×1) =P _(M×N) ·h _(N×1) +n _(M×1)

where the elements of the equation are:

y_(M×1)=is a set of M observations;

h_(N×1) is a set of N unknowns representing the vector of channelcoefficients of the UEs; each active UE corresponds to a nonzero elementin h and each inactive UE corresponds to a zero element in h. It isnoted that h is not the channel per se, but is a CS determined singlevalue that represents a fixed channel over the entire pilot sequence;following CS detection, h is the CS detection output;

P_(M×N) is a pilot matrix;

n_(M×1) is a set of noise components;

with K<<M<N, where

K is the number of nonzero elements of h, which is equal to the numberof UEs that are in fact active at a given instant;

N is the size of the pool of UEs that might be active;

M is the number of observations.

SUMMARY

According to one aspect of the present invention, there is provided amethod for detecting potentially active user equipment (UE) in a networkcomprising: performing a first iteration comprising: performing UEdetection using compressed sensing (CS) based on first pilot sequencesto detect a first set of potentially active UEs; performing channelestimation for the first set of potentially active UEs; performing atleast one subsequent iteration, each subsequent iteration comprising:performing UE detection using CS to detect a set of potentially activeUEs using results of the channel estimation from the previous iteration;performing channel estimation for the set of potentially active UEs; andafter a last iteration, outputting the set of potentially active UEsdetected in the last iteration and outputting channel estimates for theset of potentially active UEs detected in the last iteration.

According to another aspect of the present invention, there is provideda receiver for detecting potentially active user equipment (UE) in anetwork, the receiver comprising: at least one antenna; a UE detectorbased on channel estimates (UEDBCE); a channel estimator; the receiverconfigured to perform a first iteration comprising: the UEDBCEperforming UE detection using compressed sensing (CS) based on firstpilot sequences to detect a first set of potentially active UEs; thechannel estimator performing channel estimation for a first set ofpotentially active UEs; the receiver further configured to perform atleast one subsequent iteration, each subsequent iteration comprising:the UEDBCE performing UE detection using CS to detect a set ofpotentially active UEs using results of the channel estimation from theprevious iteration; the channel estimator performing channel estimationfor the set of potentially active UEs; the receiver further configuredto, after a last iteration, output the set of potentially active UEsdetected in the last iteration and output channel estimates for the setof potentially active UEs detected in last iteration.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure will now be described with reference tothe attached drawings in which:

FIG. 1 is a block diagram of a system for performing UE detection basedon channel estimates; and

FIG. 2 is a flowchart of a method of performing UE detection based onchannel estimates.

DETAILED DESCRIPTION

An assumption in the standard CS problem is that the unknowns areconstant over a set of observations. Applied to the UE detectionproblem, this leads to an assumption that the channel between each UEand the base station is fixed over an entire pilot sequence, i.e. h hasa single nonzero element for each active UE. Another feature of thestandard application of CS to UE detection is that UE detection isperformed separately from channel estimation.

The assumption that the channel between each UE and the base station isfixed over the entire pilot sequence is not necessarily true indispersive fading environments due to frequency and/or time selectivityof UEs' uplink channels.

Systems and methods are provided that make use of an iterative approachto UE detection, in which the assumption is not made that the channelbetween the UE and the base station is fixed. In addition, UE detectionand channel estimation are performed jointly rather than completelyseparately.

Referring now to FIG. 1, shown is a receiver with UE detection providedby an embodiment of the invention, generally indicated at 100. Thereceiver may, for example, be a wireless base station. It should beunderstood that the receiver is shown with components to perform UEdetection, but that typically the receiver will include otherfunctionality and components. Also shown is a set of UEs UE₁ 102, UE₂104, . . . , UE_(N) 106. More generally, the receiver 100 of FIG. 1 isconfigured to perform UE detection of active UEs within a pool of up toN UEs, but there may or may not be N UEs in the vicinity of the receiver100 at a given instant. The receiver 100 has N_(r) receive antennas 108,where N_(r)>=1. The receiver 100 has a UE detector based on channelestimates (UEDBCE) 114 that performs UE detection based on channelestimates, as a function of signals received at the N. receive antennas108. The receiver has a channel estimator 112 that performs channelestimation for UEs detected by the UEDBCE 114.

In operation, one or more of the UEs are active at a given time. Eachactive UE transmits a respective set of N_(p) pilot sequences, whereN_(p)>=1. The receiver 100 does not know which UEs transmitted a pilotsequence, and as such initially all the UEs are potentially active.However, pilot sequences will only actually be transmitted by activeUEs. The UEDBCE 114 and the channel estimator 112 together perform aniterative method of UE detection and channel estimation. During a firstiteration, the UEDBCE 114 performs UE detection based on the pilotsequences transmitted by the active UEs to detect a first set ofpotentially active UEs. The detected first set is passed to the channelestimator 112 at 113. As detailed below, a compressed sensing approachis employed for UE detection. In some embodiments, the UEDBCE 114combines the pilot sequences transmitted by the UEs with channelestimates to produce modified pilot sequences, and performs UE detectionbased on the modified pilot sequences. The channel estimator 112performs channel estimation for the first set of potentially active UEs.The channel estimates are passed to the UEDBCE at 115. Then, in each ofat least one subsequent iteration, the UEDBCE performs UE detectionusing compressed sensing to detect a set of potentially active UEs usingresults of the channel estimation from the previous iteration. Thechannel estimator 112 performs channel estimation for the new set ofpotentially active UEs. After the last iteration, the set of potentiallyactive UEs detected in the last iteration is output as the determinedset of potentially active UEs along with channel estimates for the setof potentially active UEs determined in last iteration. There may be afixed number of subsequent iterations, or the method may continue untilsome other stopping condition is satisfied that is a function of CSdetection.

Detailed example methods of performing UE detection and channelestimation that may be performed by the UEDBCE 11 and the channelestimator 112 will now be described.

In the following description, references are made to the followingterms:

-   -   primary pilot matrices: the primary pilot matrices are defined        by the actual pilot sequences transmitted by the N UEs, if they        were all active. So long as the pilot sequences transmitted by        the UEs are fixed, the primary pilot matrices do not change. In        a specific example, each UE transmits N_(p) pilot sequences of        length M, and each pilot sequence occupies M OFDM tones, an OFDM        tone being one OFDM subcarrier during one OFDM symbol. The M        tones may occupy up to M different OFDM subcarriers. The        subcarriers used for the pilot sequence may be spread across the        frequency band used by the UE. The pilot sequence may also be        spread out in time, with two or more of the M tones being in        different OFDM symbols. A combination of both frequency and time        spreading is also possible. The N_(p) pilot sequences        transmitted by a given UE may all be the same or they may be        different. Where the receiver has N, receive antennas, there are        L opportunities to observe pilot sequences transmitted by the        UE, where L=N_(p)×N_(r), and as such there are L pilot matrices        for the UEs collectively, each pilot matrix containing a column        for each UE.    -   secondary pilot matrices: the secondary pilot matrices contain        modified pilot sequences generated from the primary pilot        sequences and the channel estimates using a transformation such        as described below by way of example, although for the first        iteration, the secondary pilot matrices are set equal to the        primary pilot matrices;    -   j_(max) is a parameter defining the maximum number of        iterations;    -   the sequence K₁, . . . , K_(jmax) is a non-increasing sequence        that defines how many UEs are kept in each iteration.

In defining the sequence K₁, . . . , K_(jmax), in some embodiments K_(i)satisfies K_(i)>K. In some embodiments, K₁ is set to be significantlylarger than K in the first iteration so as not to exclude any active UEsfor the channel estimation. However, setting K₁ to be very largerelative to K may have an effect on channel estimation quality.

As iterations go on, K can be reduced because the performance ofcompressed sensing is expected to improve, and as such there is not aneed to keep a large list of potentially active UEs at the output of thecompressed sensing.

Moreover, for the number of active UEs K, the receiver could either usea priori knowledge of K or estimate the value of K (and update itsestimate) using the output of compressed sensing in each iteration.

The method of UE detection involves initialization, a first iteration,and at least one subsequent iteration. For each iteration, the steps ofUE detection, channel estimation, and secondary pilot matrix update areperformed.

Initialization:

Set j=iteration counter=1

Set the secondary pilot matrices equal to the primary pilot matrices:

P′ _(l) =P _(l) =[p _(l,1) , . . . p _(l,N) ], ∀l∈ {1, . . . , L}

Where p_(l,k)is a vector of length M representing the pilot sequencetransmitted by user k, for observation l of the L observationopportunities. For example, in a system with two receive antennas(antenna 1 and antenna 2), and two pilot sequences (pilot sequence 1 andpilot sequence 2) transmitted by a UE, the l could be incrementedaccording to:

l=1: antenna 1, pilotsequence 1

l=2: antenna 2, pilot sequence 1

l=3: antenna 1, pilot sequence 2

l=4: antenna 2, pilot sequence 2

Set the values of j_(max) and the sequence K₁, . . . , K_(jmax)

UE Detection

UE detection is performed with compressed sensing using the secondarypilot matrices (Le. using primary pilot matrices for the first iterationand modified pilot sequences for subsequent iterations) P′₁, . . . ,P′_(L):

y _(M×1[l]) =P′ _(l) ·h _(N×1) ^([l]) +n _(M×1) ^([l]) , ∀l∈{1, . . . ,L}

M: Measurement length=length of pilot sequence

L: Number of measurements

N: Pilot pool size (UE pool size)

An example setting for multicarrier uplink, such as OFDM:

M: Length of the pilot sequence=number of pilot subcarrie s

L: N_(p)=number of pilot sequence transmissions (number of times thepilot sequence is transmitted)×N_(r)=number of receive antennas.

UE detection using compressed sensing can involve solving the previouslyreferenced convex optimization problem. However, it should be understoodthat the specified convex optimization problem is just one setup toaddress the CS problem. Other approaches and setups may alternatively beemployed. Examples of techniques to solve the compressed sensing probleminclude the following algorithms (and their block versions for the caseof L>1): Orthogonal Matching Pursuit (OMP), Coordinate Descent (CD),Approximate Message Passing (AMP).

The output of CS detection for the first iteration is an estimate of avector h_(N×1) containing a respective detected signal for each UE.

The UEs are then sorted in decreasing order of the norms of theircorresponding detected signal at the output of CS detection. The firstK_(j) UEs are selected as the set of potentially active UEs.

Channel Estimation

The uplink channels of the K_(j) potentially active UEs are estimated byapplying a known channel estimation algorithm on their correspondingpilot sequences from the primary pilot matrices P₁, . . . , P_(L). Thesequence K₁, . . . , K_(jmax) is a non-increasing sequence of numbers,such that the number of potentially active UEs in each iteration is heldthe same or decreased. It is noted that the quality of the channelestimates can be expected to improve as the number of potentially activeUEs decreases. Examples of channel estimation algorithms that can beapplied include frequency domain MMSE-based and time domain MMSE-basedchannel estimations, but other approaches can be used and the methodsdescribed herein are not limited to a specific approach to channelestimation. The channel estimates are defined as:

ĥ _(l,k) , ∀l∈ {1, . . . L}k∈ {k ₁ , . . . , k _(K) _(j) }

For each UE in the current set of potentially active UEs, there is arespective channel estimate for each of the L measurements.

Secondary Pilot Matrix Update

Next, the secondary pilot matrix is updated. More specifically, thesecondary pilot sequences corresponding to the K_(j) potentially activeUEs are updated as a function of the primary pilot sequences, and thechannel estimates. In a specific example, this is done using Hadamardmultiplication, also known as element-wise multiplication.

p′ _(l,k) =p _(l,k) ∘ĥ _(l,k) , ∀l∈ {1, . . . , L}, k∈ {k ₁ , . . . , k_(K) _(j) }

followed by pilot normalization to ensure power is the same as originalpilot power:

${p_{,k}^{\prime} = {p_{,k}^{\prime} \times \frac{{p_{,k}}_{2}}{{p_{.k}^{\prime}}_{2}}}},{\forall{ \in \left\{ {1,\ldots \mspace{11mu},L} \right\}}},{k \in \left\{ {k_{1},\ldots \mspace{11mu},k_{K_{j}}} \right\}}$

After UE detection, channel estimation and secondary pilot update, j isincremented. If j≦j_(max), then the method returns to the step of UEdetection using compressed sensing. In subsequent iterations (iterationsother than the first iteration), compressed sensing is performed usingthe secondary pilot sequences which are a function of both the primarypilot sequences and the channel estimates. Otherwise, j >j_(max), andthe list of K_(jmax) potentially active UEs is output together withtheir estimated channel coefficients.

In the example described, j_(max) iterations are performed. In thiscase, the number of iterations is set a priori. In another embodiment,the receiver can stop the iterations once some UE detection performancecriterion is met. As an example of the latter approach, the receiver cansort UEs in order according to the output of compressed sensing anddetermine if there is a drop in the CS detection output between a pairof consecutive UEs in the sorted list that is greater than a predefinedthreshold. When there is such a drop then the iterations can stop.

The approach introduced above can be used to estimate the value of K, ifit is not known a priori. More specifically, the receiver can set athreshold. Then, if there is drop in the CS detection output within asorted list that is larger than a threshold, the number of UEs from thefirst UE in the sorted list up to the UE after which the drop happenscan be considered as an estimate of the number of active UEs. Thisestimate can be updated iteration by iteration.

In some embodiments, a predefined cap (maximum) on the number of activeUEs can be set by the system. Then, the minimum of this cap and thethreshold-based estimated K can be used as the number of active UEs.

In some embodiments, K_(jmax) is set to be greater than the expectednumber of active users, and a decoding process is used to make a finaldecision on the number of active users. For example, in someembodiments, the K_(jmax) is set to be at least two times the known orestimated number of actual users.

The method of UE detection is summarized in FIG. 2. The base stationperforms the method steps 200 to 212 to detect a set of UEs. In block200, the base station receives combined transmissions of a set of activeUEs over the air. In block 202, the counter j is initialized to one, anda set of secondary pilot sequences is initialized to the primary pilotsequences of all the UEs. In block 204, compressed sensing is performedusing the secondary pilot sequences. The output is a set of K_(j)potentially active UE indices. In block 204, channel estimation isperformed with the corresponding columns of the primary pilot sequencesas pilot matrices. The output is a set of channel coefficients of theK_(j) potentially active UEs. In block 208, the set of secondary pilotsequences is updated using the estimated channel coefficients. Thecounter j is incremented in block 210. If the counter j<=j_(max) inblock 212, then the method continues with another iteration by returningto block 204. Otherwise, the method ends.

The described UE detection algorithm is based on iterative CS/channelestimation so as to make a fading channel effectively flat over thepilot sequence. More specifically, the secondary pilot sequences areobtained by absorbing the channel changes into the actual (primary)pilot sequences. This is done via the Hadamard multiplication operation.Therefore, the impact of channel over the secondary pilot sequences isnow flat (or constant over the pilot sequence).

For the purpose of illustrating the application of the describedapproach an example simulation will be described. The followingsimulation setup was employed:

Pool size: 200

Pilot generation: random phase

Pilot arrangement: Frequency domain

Pilot length per pilot sequence: 50

number of active UEs: 12 (UE₁, . . . , UE₁₂)

number of pilot sequence transmissions per UE: 2

Antenna arrangement: 1×2

Channel: Exponential multi-tap fading

SNR: 30 db

CS algorithm: Generalized block coordinate descent (BCD)

number of CS iterations: 100

Channel estimation algorithm: Time domain MMSE

j_(max) (i.e. # of detection-estimation iterations): 3

(K₁, K₂, K₃): (72 , 24 , 24)

The simulation results show the results after two iterations for 10different trials. For each iteration of each trial, shown are the ranksof the 12 active UEs, i.e. UE₁, . . . , UE₁₂, in the sorted list of theoutput of compressed sensing. In this example, the receiver finallyoutputs the 12 UEs from the top of the sorted list as detected UEs. Anumber between 1 and 12 represents a correct detection and a numberlarger than 12 represents an error. When the number for the firstiteration is bold, that means there is an error. For example, in thefirst trial, first iteration, UE₈, UE₉, and UE₁₂ are ranked as 14^(th),16^(th), 13^(th), and therefore, are missed if the detection isfinalized after the first iteration. The rankings of UE₈, UE₉, and UE₁₂for the second iteration are also bolded for comparison purposes. It canbe seen that for the simulation parameters used, after two iterations,most errors have been removed.

Trial Iteration Ranks of active UEs in the sorted output of CS # # UE₁UE₂ UE₃ UE₄ UE₅ UE₆ UE₇ UE₈ UE₉ UE₁₀ UE₁₁ UE₁₂ 1 1 2 4 1 5 10 8 6 14 163 7 13 2 2 4 1 5 12 7 6 10 8 3 9 11 2 1 2 10 8 5 1 7 3 46 4 9 6 12 2 210 11 7 1 6 4 12 5 8 3 9 3 1 2 5 4 9 14 21 7 1 3 6 52 8 2 3 9 2 7 13 105 1 4 6 12 8 4 1 12 3 2 10 11 8 6 5 1 7 4 9 2 10 4 2 12 11 9 6 5 1 8 3 75 1 7 5 8 15 4 3 6 1 11 2 9 10 2 6 5 7 12 8 4 3 1 11 2 9 10 6 1 8 9 3 146 7 25 1 5 2 13 4 2 8 7 4 11 6 9 13 1 5 3 10 2 7 1 10 2 3 4 8 5 13 6 7 111 9 2 10 8 3 2 4 7 12 5 6 1 11 9 8 1 4 9 12 11 1 5 2 3 8 10 6 7 2 5 1210 7 2 4 3 1 8 9 6 11 9 1 11 12 6 8 4 9 3 7 2 10 1 5 2 10 12 6 7 4 9 2 85 11 1 3 10 1 2 5 16 4 9 7 8 13 12 3 6 1 2 3 5 12 2 8 9 7 11 10 4 6 1

The error rate after the first, second and third iteration is summarizedas follows:

After 1^(st) After 2^(nd) After 3^(rd) iteration iteration iterationDetection Error 28.9% 7.3% 7.3% Rate

Numerous modifications and variations of the present disclosure arepossible in light of the above teachings. It is therefore to beunderstood that within the scope of the appended claims, the disclosuremay be practiced otherwise than as specifically described herein.

1. A method for detecting potentially active user equipment (UE) in anetwork comprising: performing a first iteration comprising: performingUE detection using compressed sensing (CS) based on first pilotsequences to detect a first set of potentially active UEs; andperforming channel estimation for the first set of potentially activeUEs; performing at least one subsequent iteration, each subsequentiteration comprising: performing UE detection using CS to detect a setof potentially active UEs using results of the channel estimation fromthe previous iteration; and performing channel estimation for the set ofpotentially active UEs; and after a last iteration, outputting the setof potentially active UEs detected in the last iteration and outputtingchannel estimates for the set of potentially active UEs detected in thelast iteration.
 2. The method of claim 1 wherein the at least onesubsequent iteration is a predetermined number of subsequent iterations.3. The method of claim 1 wherein performing the at least one subsequentiteration comprises performing subsequent iterations until a UEdetection performance criterion is met.
 4. The method of claim 1wherein: the first iteration further comprises generating modified pilotsequences for the first set of potentially active UEs based on the firstpilot sequences of the UEs of the first set of potentially active UEsand the channel estimate for each UE of the first set of potentiallyactive UEs; wherein for each subsequent iteration: performing UEdetection using CS comprises using CS in combination with the modifiedpilot sequences generated in the previous iteration for the set ofpotentially active UEs generated in the previous iteration to generatethe set of potentially active UEs; performing channel estimationcomprises generating a channel estimate for each UE of the set ofpotentially active UEs based on the first pilot sequences of the set ofpotentially active UEs; the subsequent iteration further comprisinggenerating modified pilot sequences for the set of potentially activeUEs based on the first pilot sequences of the UEs of the set ofpotentially active UEs and the channel estimate for each UE of the setof potentially active UEs.
 5. The method of claim 4 wherein: in thefirst iteration, generating modified pilot sequences for the first setof potentially active UEs based on the first pilot sequences of the UEsof the first set of potentially active UEs and the channel estimate foreach UE of the first set of potentially active UEs comprises: usingHadamard multiplication to multiply the first pilot sequences of the UEsof the first set of potentially active UEs by the channel estimates ofthe first set of potentially active UEs and then normalizing a result ofthe Hadamard multiplication; and in each subsequent iteration generatingmodified pilot sequences for the set of potentially active UEs based onthe first pilot sequences of the UEs of the set of potentially activeUEs and the channel estimate for each UE of the set of potentiallyactive UEs comprises: using Hadamard multiplication to multiply thefirst pilot sequences of the UEs of the set of potentially active UEs bythe channel estimates of the set of potentially active UEs and thennormalizing a result of the Hadamard multiplication.
 6. The method ofclaim 4 wherein the first pilot sequences and the modified pilotsequences generated in each iteration each comprise a set of pilotmatrices, wherein a number (L) of matrices in the set of pilot matricesis defined by a number of pilot sequences transmitted by each UEmultiplied by a number of receive antennas of a device receiving thepilot sequences.
 7. The method of claim 4 wherein using CS incombination with first pilot sequences, and using CS in combination withthe modified pilot sequences, comprises using CS to solve the equation:y _(M×1) ^([l]) =P′ _(l) ×h _(N×1) ^([l]) +n _(M×1) ^([l]) ∀l∈ {1, . . .L}, wherein y is the received signal, P′ is a matrix containing thefirst pilot sequences or the modified pilot sequences, h is a channelestimate, M is a measurement length, L is a number of measurements and Nis a total number of UEs.
 8. The method of claim 4 wherein performing CSto generate each set of potentially active UEs comprises, for a set ofUEs with a detectable signal after CS detection, selecting apredetermined number of UEs of the set of UEs with a detectable signalafter CS detection as the set of potentially active UEs.
 9. The methodof claim 8 wherein selecting the predetermined number of UEs of the setof UEs with a detectable signal after CS detection as the set ofpotentially active UEs comprises: selecting the predetermined number ofUEs having the greatest norms of detected signals output from CSdetection.
 10. The method of claim 8 further comprising at least one of:using a first smaller predetermined number after the first iteration;and using a second smaller predetermined number after at least onesubsequent iteration, the second smaller predetermined number beingsmaller than the first smaller predetermined number.
 11. The method ofclaim 10 wherein a smallest predetermined number used in any iterationis set to be greater than an expected number of active users, the methodfurther comprising employing a decoding process to select a subset ofpotentially active users from the set of potentially active usersdetermined in the last iteration.
 12. A receiver for detectingpotentially active user equipment (UE) in a network, the receivercomprising: at least one antenna; a UE detector based on channelestimates (UEDBCE); a channel estimator; the receiver configured toperform a first iteration comprising: the UEDBCE performing UE detectionusing compressed sensing (CS) based on first pilot sequences to detect afirst set of potentially active UEs; the channel estimator performingchannel estimation for a first set of potentially active UEs; thereceiver further configured to perform at least one subsequentiteration, each subsequent iteration comprising: the UEDBCE performingUE detection using Cs to detect a set of potentially active UEs usingresults of the channel estimation from the previous iteration; thechannel estimator performing channel estimation for the set ofpotentially active UEs; the receiver further configured to, after a lastiteration, output the set of potentially active UEs detected in the lastiteration and output channel estimates for the set of potentially activeUEs detected in last iteration.
 13. The receiver of claim 12 wherein: inthe first iteration, generating modified pilot sequences for the firstset of potentially active UEs based on the first pilot sequences of theUEs of the first set of potentially active UEs and the channel estimatefor each UE of the first set of potentially active UEs comprises: usingHadamard multiplication to multiply the first pilot sequences of the UEsof the first set of potentially active UEs by the channel estimates ofthe first set of potentially active UEs and then normalizing a result ofthe Hadamard multiplication; and in each subsequent iteration generatingmodified pilot sequences for the set of potentially active UEs based onthe first pilot sequences of the UEs of the set of potentially activeUEs and the channel estimate for each UE of the set of potentiallyactive UEs comprises: using Hadamard multiplication to multiply thefirst pilot sequences of the UEs of the set of potentially active UEs bythe channel estimates of the set of potentially active UEs and thennormalizing a result of the Hadamard multiplication.
 14. The receiver ofclaim 12 wherein the at least one subsequent iteration is apredetermined number of subsequent iterations.
 15. The receiver of claim12 wherein: the first iteration further comprises generating modifiedpilot sequences for the first set of potentially active UEs based on thefirst pilot sequences of the UEs of the first set of potentially activeUEs and the channel estimate for each UE of the first set of potentiallyactive UEs; for each subsequent iteration: the UEDBCE performing UEdetection using CS comprises using CS in combination with the modifiedpilot sequences generated in the previous iteration for the set ofpotentially active UEs generated in the previous iteration to generatethe set of potentially active UEs; the channel estimator performingchannel estimation comprises generating a channel estimate for each UEof the set of potentially active UEs based on the first pilot sequencesof the set of potentially active UEs; the UEDBCE generating modifiedpilot sequences for the set of potentially active UEs based on the firstpilot sequences of the UEs of the set of potentially active UEs and thechannel estimate for each UE of the set of potentially active UEs. 16.The receiver of claim 15 wherein the first pilot sequences and themodified pilot sequences generated in each iteration each comprise a setof pilot matrices, wherein a number (L) of matrices in the set of pilotmatrices is defined by a number of pilot sequences transmitted by the UEmultiplied by a number of receive antennas of the receiver.
 17. Thereceiver of claim 15 wherein using CS to generate each set ofpotentially active UEs comprises, for a set of UEs with a detectablesignal after CS detection, selecting a predetermined number of UEs ofthe set of UEs with a detectable signal after CS detection as the set ofpotentially active UEs.
 18. The receiver of claim 17 wherein selectingthe predetermined number of UEs of the set of UEs with a detectablesignal after CS detection as the set of potentially active UEscomprises: selecting the predetermined number of UEs having the greatestnorms of detected signals output from CS detection.
 19. The receiver ofclaim 17 further configured to at least one of: use a first smallerpredetermined number after the first iteration; and use a second smallerpredetermined number after each of at least one subsequent iteration,the second smaller predetermined number being smaller than the firstsmaller predetermined number.
 20. The receiver of claim 19 wherein asmallest predetermined number used in any iteration is set to be greaterthan an expected number of active users, the method further comprisingemploying a decoding process to select a subset of potentially activeusers from the set of potentially active users determined in the lastiteration.